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Hierarchical models provide a useful framework for the complexities encountered in policy-relevant research in which the impact of social programs is being assessed. Such complexities include multi-site data, censored data and over-dispersion. In this paper, Bayesian inference through Markov Chain Monte Carlo methods is used for the analysis of a complex hierarchical log-normal model that shows the impact of a managed care strategy aimed at limiting length of hospital stays. Parameters in this model allow for variability in baseline length-of-stay as well as the program effect across hospitals. The authors demonstrate elicitation and sensitivity analysis with respect to prior distributions. All calculations for the posterior and predictive distributions were obtained using the software BUGS. 相似文献
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The National Institute of Mental Health (NIMH) Collaborative Study of Long-Term Maintenance Drug Therapy in Recurrent Affective
Illness was a multicenter randomized controlled clinical trial designed to determine the efficacy of a pharmacotherapy for
the prevention of the recurrence of unipolar affective disorders. The outcome of interest in this study was the time until
the recurrence of a depressive episode. The data show much heterogeneity between centers for the placebo group. The aim of
this paper is to use Bayesian hierarchical survival models to investigate the heterogeneity of placebo effects among centers
in the NIMH study. This heterogeneity is explored in terms of the marginal posterior distributions of parameters of interest
and predictive distributions of future observations. The Gibbs sampling algorithm is used to approximate posterior and predictive
distributions. Sensitivity of results to the assumption of a constant hazard survival distribution at the first stage of the
hierarchy is examined by comparing results derived from a two component exponential mixture and a two component exponential
changepoint model to the results derived from an exponential model. The second component of the mixture and changepoint models
is assumed to be a surviving fraction. For each of these first stage parametric models sensitivity of results to second stage
prior distributions is also examined.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
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